Metanalysis of performance in condition of Stereotype Boost (stBoost)

Geiser C. Challco geiser@alumni.usp.br

Initial Variables and Loading Data

cond <- "stBoost"
to_remove <- c('S11')
sub.groups <- c("country","age","ed.level","intervention",
                "country:age","country:ed.level","country:intervention",
                "age:intervention","ed.level:intervention",
                "country:age:intervention","country:ed.level:intervention")
dat <- read_excel("../data/data-without-outliers.xlsx", sheet = "perform-cond-descriptive")
dat <- dat[!dat$study %in% to_remove, ]

leg <- read_excel("../data/data-without-outliers.xlsx", sheet = "legend")
## New names:
## • `` -> `...10`
leg <- leg[!leg$study %in% to_remove, ]

idx.e <- which(dat$condition==cond)
idx.c <- which(dat$condition=="control")

data <- data.frame(
  study = dat$study[idx.c],
  n.e = dat$N[idx.e], mean.e = dat$M[idx.e], sd.e = dat$SD[idx.e],
  n.c = dat$N[idx.c], mean.c = dat$M[idx.c], sd.c = dat$SD[idx.c]
)
for (cgroups in strsplit(sub.groups,":")) {
  data[[paste0(cgroups, collapse = ":")]] <- sapply(data$study, FUN = function(x) {
    paste0(sapply(cgroups, FUN = function(namecol) leg[[namecol]][which(x == leg$study)]), collapse = ":")
  })
}
data[["lbl"]] <- sapply(data$study, FUN = function(x) leg$Note[which(x == leg$study)])

Perform meta-analyses

m.cont <- metacont(
  n.e = n.e, mean.e = mean.e, sd.e = sd.e, n.c = n.c, mean.c = mean.c, sd.c = sd.c,
  studlab = lbl, data = data, sm = "SMD", method.smd = "Hedges",
  fixed = F, random = T, method.tau = "REML", hakn = T, title = paste("Performance in",cond)
)
summary(m.cont)
## Review:     Performance in stBoost
## 
##                                    SMD            95%-CI %W(random)
## S1                              0.0615 [-0.4388; 0.5618]        8.4
## S2                             -0.1670 [-0.6440; 0.3101]        9.2
## S3                             -0.2975 [-0.8356; 0.2405]        7.2
## S4                              0.3656 [-0.1862; 0.9173]        6.9
## S5                             -0.0315 [-0.4473; 0.3843]       12.1
## S6                              0.2023 [-0.2451; 0.6496]       10.5
## S7                              0.2952 [-0.1052; 0.6957]       13.1
## S8: Conducted by BNU           -0.1339 [-0.6179; 0.3501]        9.0
## S9: Albuquerque, et al. (2017)  0.3264 [-0.0970; 0.7498]       11.7
## S10: Only use prompt msgs       0.2641 [-0.1544; 0.6827]       12.0
## 
## Number of studies combined: k = 10
## Number of observations: o = 748
## 
##                         SMD            95%-CI    t p-value
## Random effects model 0.1071 [-0.0532; 0.2675] 1.51  0.1649
## 
## Quantifying heterogeneity:
##  tau^2 = 0 [0.0000; 0.1227]; tau = 0 [0.0000; 0.3502]
##  I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
## 
## Test of heterogeneity:
##     Q d.f. p-value
##  8.29    9  0.5055
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(m.cont, digits=2, digits.sd = 2, test.overall = T, label.e = cond)

Subgroup analysis by “country”

m.sg4sub <- update.meta(m.cont, subgroup = country, random = T, fixed = F)
summary(m.sg4sub)
## Review:     Performance in stBoost
## 
##                                    SMD            95%-CI %W(random) country
## S1                              0.0615 [-0.4388; 0.5618]        8.4  Brazil
## S2                             -0.1670 [-0.6440; 0.3101]        9.2  Brazil
## S3                             -0.2975 [-0.8356; 0.2405]        7.2  Brazil
## S4                              0.3656 [-0.1862; 0.9173]        6.9  Brazil
## S5                             -0.0315 [-0.4473; 0.3843]       12.1  Brazil
## S6                              0.2023 [-0.2451; 0.6496]       10.5  Brazil
## S7                              0.2952 [-0.1052; 0.6957]       13.1  Brazil
## S8: Conducted by BNU           -0.1339 [-0.6179; 0.3501]        9.0   China
## S9: Albuquerque, et al. (2017)  0.3264 [-0.0970; 0.7498]       11.7  Brazil
## S10: Only use prompt msgs       0.2641 [-0.1544; 0.6827]       12.0  Brazil
## 
## Number of studies combined: k = 10
## Number of observations: o = 748
## 
##                         SMD            95%-CI    t p-value
## Random effects model 0.1071 [-0.0532; 0.2675] 1.51  0.1649
## 
## Quantifying heterogeneity:
##  tau^2 = 0 [0.0000; 0.1227]; tau = 0 [0.0000; 0.3502]
##  I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
## 
## Test of heterogeneity:
##     Q d.f. p-value
##  8.29    9  0.5055
## 
## Results for subgroups (random effects model):
##                    k     SMD            95%-CI tau^2 tau    Q  I^2
## country = Brazil   9  0.1308 [-0.0390; 0.3007]     0   0 7.24 0.0%
## country = China    1 -0.1339 [-0.6179; 0.3501]    --  -- 0.00   --
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   1.06    1  0.3042
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)

Subgroup analysis by “age”

m.sg4sub <- update.meta(m.cont, subgroup = country, random = T, fixed = F)
summary(m.sg4sub)
## Review:     Performance in stBoost
## 
##                                    SMD            95%-CI %W(random) country
## S1                              0.0615 [-0.4388; 0.5618]        8.4  Brazil
## S2                             -0.1670 [-0.6440; 0.3101]        9.2  Brazil
## S3                             -0.2975 [-0.8356; 0.2405]        7.2  Brazil
## S4                              0.3656 [-0.1862; 0.9173]        6.9  Brazil
## S5                             -0.0315 [-0.4473; 0.3843]       12.1  Brazil
## S6                              0.2023 [-0.2451; 0.6496]       10.5  Brazil
## S7                              0.2952 [-0.1052; 0.6957]       13.1  Brazil
## S8: Conducted by BNU           -0.1339 [-0.6179; 0.3501]        9.0   China
## S9: Albuquerque, et al. (2017)  0.3264 [-0.0970; 0.7498]       11.7  Brazil
## S10: Only use prompt msgs       0.2641 [-0.1544; 0.6827]       12.0  Brazil
## 
## Number of studies combined: k = 10
## Number of observations: o = 748
## 
##                         SMD            95%-CI    t p-value
## Random effects model 0.1071 [-0.0532; 0.2675] 1.51  0.1649
## 
## Quantifying heterogeneity:
##  tau^2 = 0 [0.0000; 0.1227]; tau = 0 [0.0000; 0.3502]
##  I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
## 
## Test of heterogeneity:
##     Q d.f. p-value
##  8.29    9  0.5055
## 
## Results for subgroups (random effects model):
##                    k     SMD            95%-CI tau^2 tau    Q  I^2
## country = Brazil   9  0.1308 [-0.0390; 0.3007]     0   0 7.24 0.0%
## country = China    1 -0.1339 [-0.6179; 0.3501]    --  -- 0.00   --
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   1.06    1  0.3042
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)

Subgroup analysis by “ed.level”

m.sg4sub <- update.meta(m.cont, subgroup = ed.level, random = T, fixed = F)
summary(m.sg4sub)
## Review:     Performance in stBoost
## 
##                                    SMD            95%-CI %W(random)         ed.level
## S1                              0.0615 [-0.4388; 0.5618]        8.4  upper-secundary
## S2                             -0.1670 [-0.6440; 0.3101]        9.2  upper-secundary
## S3                             -0.2975 [-0.8356; 0.2405]        7.2  upper-secundary
## S4                              0.3656 [-0.1862; 0.9173]        6.9 higher-education
## S5                             -0.0315 [-0.4473; 0.3843]       12.1 higher-education
## S6                              0.2023 [-0.2451; 0.6496]       10.5 higher-education
## S7                              0.2952 [-0.1052; 0.6957]       13.1          unknown
## S8: Conducted by BNU           -0.1339 [-0.6179; 0.3501]        9.0          unknown
## S9: Albuquerque, et al. (2017)  0.3264 [-0.0970; 0.7498]       11.7          unknown
## S10: Only use prompt msgs       0.2641 [-0.1544; 0.6827]       12.0  upper-secundary
## 
## Number of studies combined: k = 10
## Number of observations: o = 748
## 
##                         SMD            95%-CI    t p-value
## Random effects model 0.1071 [-0.0532; 0.2675] 1.51  0.1649
## 
## Quantifying heterogeneity:
##  tau^2 = 0 [0.0000; 0.1227]; tau = 0 [0.0000; 0.3502]
##  I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
## 
## Test of heterogeneity:
##     Q d.f. p-value
##  8.29    9  0.5055
## 
## Results for subgroups (random effects model):
##                               k     SMD            95%-CI  tau^2    tau    Q   I^2
## ed.level = upper-secundary    4 -0.0049 [-0.4065; 0.3967] 0.0082 0.0905 3.23  7.2%
## ed.level = higher-education   3  0.1443 [-0.3400; 0.6285]      0      0 1.37  0.0%
## ed.level = unknown            3  0.1903 [-0.4104; 0.7910] 0.0036 0.0599 2.38 16.1%
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   1.25    2  0.5347
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)

Subgroup analysis by “intervention”

m.sg4sub <- update.meta(m.cont, subgroup = `intervention`, random = T, fixed = F)
summary(m.sg4sub)
## Review:     Performance in stBoost
## 
##                                    SMD            95%-CI %W(random)
## S1                              0.0615 [-0.4388; 0.5618]        8.4
## S2                             -0.1670 [-0.6440; 0.3101]        9.2
## S3                             -0.2975 [-0.8356; 0.2405]        7.2
## S4                              0.3656 [-0.1862; 0.9173]        6.9
## S5                             -0.0315 [-0.4473; 0.3843]       12.1
## S6                              0.2023 [-0.2451; 0.6496]       10.5
## S7                              0.2952 [-0.1052; 0.6957]       13.1
## S8: Conducted by BNU           -0.1339 [-0.6179; 0.3501]        9.0
## S9: Albuquerque, et al. (2017)  0.3264 [-0.0970; 0.7498]       11.7
## S10: Only use prompt msgs       0.2641 [-0.1544; 0.6827]       12.0
##                                                                        intervention
## S1                             Gender-stereotype color, ranking, badges, and avatar
## S2                             Gender-stereotype color, ranking, badges, and avatar
## S3                             Gender-stereotype color, ranking, badges, and avatar
## S4                             Gender-stereotype color, ranking, badges, and avatar
## S5                             Gender-stereotype color, ranking, badges, and avatar
## S6                             Gender-stereotype color, ranking, badges, and avatar
## S7                             Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU           Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs           Gender-stereotyped motivational message prompts
## 
## Number of studies combined: k = 10
## Number of observations: o = 748
## 
##                         SMD            95%-CI    t p-value
## Random effects model 0.1071 [-0.0532; 0.2675] 1.51  0.1649
## 
## Quantifying heterogeneity:
##  tau^2 = 0 [0.0000; 0.1227]; tau = 0 [0.0000; 0.3502]
##  I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
## 
## Test of heterogeneity:
##     Q d.f. p-value
##  8.29    9  0.5055
## 
## Results for subgroups (random effects model):
##                                                      k    SMD            95%-CI tau^2 tau    Q  I^2
## intervention = Gender-stereotype color, rankin ...   9 0.0858 [-0.0920; 0.2636]     0   0 7.67 0.0%
## intervention = Gender-stereotyped motivational ...   1 0.2641 [-0.1544; 0.6827]    --  -- 0.00   --
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   0.62    1  0.4323
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)

Subgroup analysis by “country:age”

m.sg4sub <- update.meta(m.cont, subgroup = `country:age`, random = T, fixed = F)
summary(m.sg4sub)
## Review:     Performance in stBoost
## 
##                                    SMD            95%-CI %W(random)           country:age
## S1                              0.0615 [-0.4388; 0.5618]        8.4     Brazil:adolescent
## S2                             -0.1670 [-0.6440; 0.3101]        9.2     Brazil:adolescent
## S3                             -0.2975 [-0.8356; 0.2405]        7.2     Brazil:adolescent
## S4                              0.3656 [-0.1862; 0.9173]        6.9          Brazil:adult
## S5                             -0.0315 [-0.4473; 0.3843]       12.1          Brazil:adult
## S6                              0.2023 [-0.2451; 0.6496]       10.5          Brazil:adult
## S7                              0.2952 [-0.1052; 0.6957]       13.1          Brazil:adult
## S8: Conducted by BNU           -0.1339 [-0.6179; 0.3501]        9.0  China:no-restriction
## S9: Albuquerque, et al. (2017)  0.3264 [-0.0970; 0.7498]       11.7 Brazil:no-restriction
## S10: Only use prompt msgs       0.2641 [-0.1544; 0.6827]       12.0    Brazil:adolescence
## 
## Number of studies combined: k = 10
## Number of observations: o = 748
## 
##                         SMD            95%-CI    t p-value
## Random effects model 0.1071 [-0.0532; 0.2675] 1.51  0.1649
## 
## Quantifying heterogeneity:
##  tau^2 = 0 [0.0000; 0.1227]; tau = 0 [0.0000; 0.3502]
##  I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
## 
## Test of heterogeneity:
##     Q d.f. p-value
##  8.29    9  0.5055
## 
## Results for subgroups (random effects model):
##                                       k     SMD            95%-CI tau^2 tau    Q  I^2
## country:age = Brazil:adolescent       3 -0.1280 [-0.5695; 0.3135]     0   0 0.96 0.0%
## country:age = Brazil:adult            4  0.1906 [-0.0844; 0.4656]     0   0 1.75 0.0%
## country:age = China:no-restriction    1 -0.1339 [-0.6179; 0.3501]    --  -- 0.00   --
## country:age = Brazil:no-restriction   1  0.3264 [-0.0970; 0.7498]    --  -- 0.00   --
## country:age = Brazil:adolescence      1  0.2641 [-0.1544; 0.6827]    --  -- 0.00   --
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   8.54    4  0.0737
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)

Subgroup analysis by “country:ed.level”

m.sg4sub <- update.meta(m.cont, subgroup = `country:ed.level`, random = T, fixed = F)
summary(m.sg4sub)
## Review:     Performance in stBoost
## 
##                                    SMD            95%-CI %W(random)        country:ed.level
## S1                              0.0615 [-0.4388; 0.5618]        8.4  Brazil:upper-secundary
## S2                             -0.1670 [-0.6440; 0.3101]        9.2  Brazil:upper-secundary
## S3                             -0.2975 [-0.8356; 0.2405]        7.2  Brazil:upper-secundary
## S4                              0.3656 [-0.1862; 0.9173]        6.9 Brazil:higher-education
## S5                             -0.0315 [-0.4473; 0.3843]       12.1 Brazil:higher-education
## S6                              0.2023 [-0.2451; 0.6496]       10.5 Brazil:higher-education
## S7                              0.2952 [-0.1052; 0.6957]       13.1          Brazil:unknown
## S8: Conducted by BNU           -0.1339 [-0.6179; 0.3501]        9.0           China:unknown
## S9: Albuquerque, et al. (2017)  0.3264 [-0.0970; 0.7498]       11.7          Brazil:unknown
## S10: Only use prompt msgs       0.2641 [-0.1544; 0.6827]       12.0  Brazil:upper-secundary
## 
## Number of studies combined: k = 10
## Number of observations: o = 748
## 
##                         SMD            95%-CI    t p-value
## Random effects model 0.1071 [-0.0532; 0.2675] 1.51  0.1649
## 
## Quantifying heterogeneity:
##  tau^2 = 0 [0.0000; 0.1227]; tau = 0 [0.0000; 0.3502]
##  I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
## 
## Test of heterogeneity:
##     Q d.f. p-value
##  8.29    9  0.5055
## 
## Results for subgroups (random effects model):
##                                              k     SMD            95%-CI  tau^2    tau    Q  I^2
## country:ed.level = Brazil:upper-secundary    4 -0.0049 [-0.4065; 0.3967] 0.0082 0.0905 3.23 7.2%
## country:ed.level = Brazil:higher-education   3  0.1443 [-0.3400; 0.6285]      0      0 1.37 0.0%
## country:ed.level = Brazil:unknown            2  0.3099 [ 0.1119; 0.5080]      0      0 0.01 0.0%
## country:ed.level = China:unknown             1 -0.1339 [-0.6179; 0.3501]     --     -- 0.00   --
## 
## Test for subgroup differences (random effects model):
##                      Q d.f. p-value
## Between groups   11.25    3  0.0105
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)

Subgroup analysis by “country:intervention”

m.sg4sub <- update.meta(m.cont, subgroup = `country:intervention`, random = T, fixed = F)
summary(m.sg4sub)
## Review:     Performance in stBoost
## 
##                                    SMD            95%-CI %W(random)
## S1                              0.0615 [-0.4388; 0.5618]        8.4
## S2                             -0.1670 [-0.6440; 0.3101]        9.2
## S3                             -0.2975 [-0.8356; 0.2405]        7.2
## S4                              0.3656 [-0.1862; 0.9173]        6.9
## S5                             -0.0315 [-0.4473; 0.3843]       12.1
## S6                              0.2023 [-0.2451; 0.6496]       10.5
## S7                              0.2952 [-0.1052; 0.6957]       13.1
## S8: Conducted by BNU           -0.1339 [-0.6179; 0.3501]        9.0
## S9: Albuquerque, et al. (2017)  0.3264 [-0.0970; 0.7498]       11.7
## S10: Only use prompt msgs       0.2641 [-0.1544; 0.6827]       12.0
##                                                                       country:intervention
## S1                             Brazil:Gender-stereotype color, ranking, badges, and avatar
## S2                             Brazil:Gender-stereotype color, ranking, badges, and avatar
## S3                             Brazil:Gender-stereotype color, ranking, badges, and avatar
## S4                             Brazil:Gender-stereotype color, ranking, badges, and avatar
## S5                             Brazil:Gender-stereotype color, ranking, badges, and avatar
## S6                             Brazil:Gender-stereotype color, ranking, badges, and avatar
## S7                             Brazil:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU            China:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) Brazil:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs           Brazil:Gender-stereotyped motivational message prompts
## 
## Number of studies combined: k = 10
## Number of observations: o = 748
## 
##                         SMD            95%-CI    t p-value
## Random effects model 0.1071 [-0.0532; 0.2675] 1.51  0.1649
## 
## Quantifying heterogeneity:
##  tau^2 = 0 [0.0000; 0.1227]; tau = 0 [0.0000; 0.3502]
##  I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
## 
## Test of heterogeneity:
##     Q d.f. p-value
##  8.29    9  0.5055
## 
## Results for subgroups (random effects model):
##                                                              k     SMD            95%-CI   tau^2    tau    Q
## country:intervention = Brazil:Gender-stereotype color, ...   8  0.1107 [-0.0828; 0.3042] <0.0001 0.0002 6.79
## country:intervention = China:Gender-stereotype color,  ...   1 -0.1339 [-0.6179; 0.3501]      --     -- 0.00
## country:intervention = Brazil:Gender-stereotyped motiv ...   1  0.2641 [-0.1544; 0.6827]      --     -- 0.00
##                                                             I^2
## country:intervention = Brazil:Gender-stereotype color, ... 0.0%
## country:intervention = China:Gender-stereotype color,  ...   --
## country:intervention = Brazil:Gender-stereotyped motiv ...   --
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   1.49    2  0.4736
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)

Subgroup analysis by “age:intervention”

m.sg4sub <- update.meta(m.cont, subgroup = `age:intervention`, random = T, fixed = F)
summary(m.sg4sub)
## Review:     Performance in stBoost
## 
##                                    SMD            95%-CI %W(random)
## S1                              0.0615 [-0.4388; 0.5618]        8.4
## S2                             -0.1670 [-0.6440; 0.3101]        9.2
## S3                             -0.2975 [-0.8356; 0.2405]        7.2
## S4                              0.3656 [-0.1862; 0.9173]        6.9
## S5                             -0.0315 [-0.4473; 0.3843]       12.1
## S6                              0.2023 [-0.2451; 0.6496]       10.5
## S7                              0.2952 [-0.1052; 0.6957]       13.1
## S8: Conducted by BNU           -0.1339 [-0.6179; 0.3501]        9.0
## S9: Albuquerque, et al. (2017)  0.3264 [-0.0970; 0.7498]       11.7
## S10: Only use prompt msgs       0.2641 [-0.1544; 0.6827]       12.0
##                                                                                   age:intervention
## S1                                 adolescent:Gender-stereotype color, ranking, badges, and avatar
## S2                                 adolescent:Gender-stereotype color, ranking, badges, and avatar
## S3                                 adolescent:Gender-stereotype color, ranking, badges, and avatar
## S4                                      adult:Gender-stereotype color, ranking, badges, and avatar
## S5                                      adult:Gender-stereotype color, ranking, badges, and avatar
## S6                                      adult:Gender-stereotype color, ranking, badges, and avatar
## S7                                      adult:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU           no-restriction:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) no-restriction:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs              adolescence:Gender-stereotyped motivational message prompts
## 
## Number of studies combined: k = 10
## Number of observations: o = 748
## 
##                         SMD            95%-CI    t p-value
## Random effects model 0.1071 [-0.0532; 0.2675] 1.51  0.1649
## 
## Quantifying heterogeneity:
##  tau^2 = 0 [0.0000; 0.1227]; tau = 0 [0.0000; 0.3502]
##  I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
## 
## Test of heterogeneity:
##     Q d.f. p-value
##  8.29    9  0.5055
## 
## Results for subgroups (random effects model):
##                                                          k     SMD            95%-CI  tau^2    tau    Q
## age:intervention = adolescent:Gender-stereotype co ...   3 -0.1280 [-0.5695; 0.3135]      0      0 0.96
## age:intervention = adult:Gender-stereotype color,  ...   4  0.1906 [-0.0844; 0.4656]      0      0 1.75
## age:intervention = no-restriction:Gender-stereotyp ...   2  0.1118 [-2.8060; 3.0296] 0.0521 0.2283 1.97
## age:intervention = adolescence:Gender-stereotyped  ...   1  0.2641 [-0.1544; 0.6827]     --     -- 0.00
##                                                          I^2
## age:intervention = adolescent:Gender-stereotype co ...  0.0%
## age:intervention = adult:Gender-stereotype color,  ...  0.0%
## age:intervention = no-restriction:Gender-stereotyp ... 49.2%
## age:intervention = adolescence:Gender-stereotyped  ...    --
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   6.51    3  0.0892
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)

Subgroup analysis by “ed.level:intervention”

m.sg4sub <- update.meta(m.cont, subgroup = `ed.level:intervention`, random = T, fixed = F)
summary(m.sg4sub)
## Review:     Performance in stBoost
## 
##                                    SMD            95%-CI %W(random)
## S1                              0.0615 [-0.4388; 0.5618]        8.4
## S2                             -0.1670 [-0.6440; 0.3101]        9.2
## S3                             -0.2975 [-0.8356; 0.2405]        7.2
## S4                              0.3656 [-0.1862; 0.9173]        6.9
## S5                             -0.0315 [-0.4473; 0.3843]       12.1
## S6                              0.2023 [-0.2451; 0.6496]       10.5
## S7                              0.2952 [-0.1052; 0.6957]       13.1
## S8: Conducted by BNU           -0.1339 [-0.6179; 0.3501]        9.0
## S9: Albuquerque, et al. (2017)  0.3264 [-0.0970; 0.7498]       11.7
## S10: Only use prompt msgs       0.2641 [-0.1544; 0.6827]       12.0
##                                                                                ed.level:intervention
## S1                              upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S2                              upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S3                              upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S4                             higher-education:Gender-stereotype color, ranking, badges, and avatar
## S5                             higher-education:Gender-stereotype color, ranking, badges, and avatar
## S6                             higher-education:Gender-stereotype color, ranking, badges, and avatar
## S7                                      unknown:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU                    unknown:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017)          unknown:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs            upper-secundary:Gender-stereotyped motivational message prompts
## 
## Number of studies combined: k = 10
## Number of observations: o = 748
## 
##                         SMD            95%-CI    t p-value
## Random effects model 0.1071 [-0.0532; 0.2675] 1.51  0.1649
## 
## Quantifying heterogeneity:
##  tau^2 = 0 [0.0000; 0.1227]; tau = 0 [0.0000; 0.3502]
##  I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
## 
## Test of heterogeneity:
##     Q d.f. p-value
##  8.29    9  0.5055
## 
## Results for subgroups (random effects model):
##                                                               k     SMD            95%-CI  tau^2    tau    Q
## ed.level:intervention = upper-secundary:Gender-stereoty ...   3 -0.1280 [-0.5695; 0.3135]      0      0 0.96
## ed.level:intervention = higher-education:Gender-stereot ...   3  0.1443 [-0.3400; 0.6285]      0      0 1.37
## ed.level:intervention = unknown:Gender-stereotype color ...   3  0.1903 [-0.4104; 0.7910] 0.0036 0.0599 2.38
## ed.level:intervention = upper-secundary:Gender-stereoty ...   1  0.2641 [-0.1544; 0.6827]     --     -- 0.00
##                                                               I^2
## ed.level:intervention = upper-secundary:Gender-stereoty ...  0.0%
## ed.level:intervention = higher-education:Gender-stereot ...  0.0%
## ed.level:intervention = unknown:Gender-stereotype color ... 16.1%
## ed.level:intervention = upper-secundary:Gender-stereoty ...    --
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   5.70    3  0.1270
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)

Subgroup analysis by “country:age:intervention”

m.sg4sub <- update.meta(m.cont, subgroup = `country:age:intervention`, random = T, fixed = F)
summary(m.sg4sub)
## Review:     Performance in stBoost
## 
##                                    SMD            95%-CI %W(random)
## S1                              0.0615 [-0.4388; 0.5618]        8.4
## S2                             -0.1670 [-0.6440; 0.3101]        9.2
## S3                             -0.2975 [-0.8356; 0.2405]        7.2
## S4                              0.3656 [-0.1862; 0.9173]        6.9
## S5                             -0.0315 [-0.4473; 0.3843]       12.1
## S6                              0.2023 [-0.2451; 0.6496]       10.5
## S7                              0.2952 [-0.1052; 0.6957]       13.1
## S8: Conducted by BNU           -0.1339 [-0.6179; 0.3501]        9.0
## S9: Albuquerque, et al. (2017)  0.3264 [-0.0970; 0.7498]       11.7
## S10: Only use prompt msgs       0.2641 [-0.1544; 0.6827]       12.0
##                                                                                  country:age:intervention
## S1                                 Brazil:adolescent:Gender-stereotype color, ranking, badges, and avatar
## S2                                 Brazil:adolescent:Gender-stereotype color, ranking, badges, and avatar
## S3                                 Brazil:adolescent:Gender-stereotype color, ranking, badges, and avatar
## S4                                      Brazil:adult:Gender-stereotype color, ranking, badges, and avatar
## S5                                      Brazil:adult:Gender-stereotype color, ranking, badges, and avatar
## S6                                      Brazil:adult:Gender-stereotype color, ranking, badges, and avatar
## S7                                      Brazil:adult:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU            China:no-restriction:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) Brazil:no-restriction:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs              Brazil:adolescence:Gender-stereotyped motivational message prompts
## 
## Number of studies combined: k = 10
## Number of observations: o = 748
## 
##                         SMD            95%-CI    t p-value
## Random effects model 0.1071 [-0.0532; 0.2675] 1.51  0.1649
## 
## Quantifying heterogeneity:
##  tau^2 = 0 [0.0000; 0.1227]; tau = 0 [0.0000; 0.3502]
##  I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
## 
## Test of heterogeneity:
##     Q d.f. p-value
##  8.29    9  0.5055
## 
## Results for subgroups (random effects model):
##                                                                  k     SMD            95%-CI tau^2 tau    Q
## country:age:intervention = Brazil:adolescent:Gender-stereo ...   3 -0.1280 [-0.5695; 0.3135]     0   0 0.96
## country:age:intervention = Brazil:adult:Gender-stereotype  ...   4  0.1906 [-0.0844; 0.4656]     0   0 1.75
## country:age:intervention = China:no-restriction:Gender-ste ...   1 -0.1339 [-0.6179; 0.3501]    --  -- 0.00
## country:age:intervention = Brazil:no-restriction:Gender-st ...   1  0.3264 [-0.0970; 0.7498]    --  -- 0.00
## country:age:intervention = Brazil:adolescence:Gender-stere ...   1  0.2641 [-0.1544; 0.6827]    --  -- 0.00
##                                                                 I^2
## country:age:intervention = Brazil:adolescent:Gender-stereo ... 0.0%
## country:age:intervention = Brazil:adult:Gender-stereotype  ... 0.0%
## country:age:intervention = China:no-restriction:Gender-ste ...   --
## country:age:intervention = Brazil:no-restriction:Gender-st ...   --
## country:age:intervention = Brazil:adolescence:Gender-stere ...   --
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   8.54    4  0.0737
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)

Subgroup analysis by “country:ed.level:intervention”

m.sg4sub <- update.meta(m.cont, subgroup = `country:ed.level:intervention`, random = T, fixed = F)
summary(m.sg4sub)
## Review:     Performance in stBoost
## 
##                                    SMD            95%-CI %W(random)
## S1                              0.0615 [-0.4388; 0.5618]        8.4
## S2                             -0.1670 [-0.6440; 0.3101]        9.2
## S3                             -0.2975 [-0.8356; 0.2405]        7.2
## S4                              0.3656 [-0.1862; 0.9173]        6.9
## S5                             -0.0315 [-0.4473; 0.3843]       12.1
## S6                              0.2023 [-0.2451; 0.6496]       10.5
## S7                              0.2952 [-0.1052; 0.6957]       13.1
## S8: Conducted by BNU           -0.1339 [-0.6179; 0.3501]        9.0
## S9: Albuquerque, et al. (2017)  0.3264 [-0.0970; 0.7498]       11.7
## S10: Only use prompt msgs       0.2641 [-0.1544; 0.6827]       12.0
##                                                                               country:ed.level:intervention
## S1                              Brazil:upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S2                              Brazil:upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S3                              Brazil:upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S4                             Brazil:higher-education:Gender-stereotype color, ranking, badges, and avatar
## S5                             Brazil:higher-education:Gender-stereotype color, ranking, badges, and avatar
## S6                             Brazil:higher-education:Gender-stereotype color, ranking, badges, and avatar
## S7                                      Brazil:unknown:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU                     China:unknown:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017)          Brazil:unknown:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs            Brazil:upper-secundary:Gender-stereotyped motivational message prompts
## 
## Number of studies combined: k = 10
## Number of observations: o = 748
## 
##                         SMD            95%-CI    t p-value
## Random effects model 0.1071 [-0.0532; 0.2675] 1.51  0.1649
## 
## Quantifying heterogeneity:
##  tau^2 = 0 [0.0000; 0.1227]; tau = 0 [0.0000; 0.3502]
##  I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
## 
## Test of heterogeneity:
##     Q d.f. p-value
##  8.29    9  0.5055
## 
## Results for subgroups (random effects model):
##                                                                       k     SMD            95%-CI tau^2 tau
## country:ed.level:intervention = Brazil:upper-secundary:Gender-s ...   3 -0.1280 [-0.5695; 0.3135]     0   0
## country:ed.level:intervention = Brazil:higher-education:Gender- ...   3  0.1443 [-0.3400; 0.6285]     0   0
## country:ed.level:intervention = Brazil:unknown:Gender-stereotyp ...   2  0.3099 [ 0.1119; 0.5080]     0   0
## country:ed.level:intervention = China:unknown:Gender-stereotype ...   1 -0.1339 [-0.6179; 0.3501]    --  --
## country:ed.level:intervention = Brazil:upper-secundary:Gender-s ...   1  0.2641 [-0.1544; 0.6827]    --  --
##                                                                        Q  I^2
## country:ed.level:intervention = Brazil:upper-secundary:Gender-s ... 0.96 0.0%
## country:ed.level:intervention = Brazil:higher-education:Gender- ... 1.37 0.0%
## country:ed.level:intervention = Brazil:unknown:Gender-stereotyp ... 0.01 0.0%
## country:ed.level:intervention = China:unknown:Gender-stereotype ... 0.00   --
## country:ed.level:intervention = Brazil:upper-secundary:Gender-s ... 0.00   --
## 
## Test for subgroup differences (random effects model):
##                      Q d.f. p-value
## Between groups   22.75    4  0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)

Funnel Plot

m.cont <- update.meta(m.cont, studlab = data$study)
summary(eggers.test(x = m.cont))
## Eggers' test of the intercept 
## ============================= 
## 
##  intercept       95% CI      t    p
##     -3.692 -9.15 - 1.77 -1.325 0.22
## 
## Eggers' test does not indicate the presence of funnel plot asymmetry.
funnel(m.cont, xlab = "Hedges' g", studlab = T, legend=T, addtau2 = T)